Supporting self-evaluation in local government via Knowledge Discovery and Data mining
| Autor: | Dean F. Duncan, C. Joy Stewart, Hye-Chung Kum |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Government
Knowledge management Sociology and Political Science business.industry Social Welfare Library and Information Sciences computer.software_genre Knowledge extraction Local government Business sector Information system Business Information flow (information theory) Data mining Information infrastructure Law computer |
| Zdroj: | Government Information Quarterly. 26:295-304 |
| ISSN: | 0740-624X |
| DOI: | 10.1016/j.giq.2008.12.009 |
| Popis: | The business sector has already recognized the importance of information flow for good management, with many businesses adopting new technology in data mining and data warehousing for intelligent operation based on free flow of information. Free flow of information in government agencies is just as important. For example, in child welfare, entities that fund social services programs have increasingly demanded improved outcomes for clients in return for continued financial support. To this end, most child welfare agencies are paying more attention to the outcomes of children in their care. In North Carolina, many county departments of social services have successfully adopted the self-evaluation model to monitor the effects of their programs on the outcomes of children. Such efforts in self-evaluation require good information flow from state division of social services to county departments of social services. In this paper, we propose a comprehensive KDD (Knowledge Discovery and Data mining) information system that could upgrade information flow in government agencies. We present the key elements of the information system and demonstrate how such a system could be successfully implemented via a case study in North Carolina. The next generation infrastructure in digital government must incorporate such information system to enable effective information flow in government agencies without compromising individual privacy. |
| Databáze: | OpenAIRE |
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